Abstract

This study compares published algorithms for the detection of bone diseases particularly osteoporosis (which is characterized by low level of bone mineral density and porosity due to microarchitectural deterioration) with claimed accuracy on based on the author selected dataset. In this study common dataset is used to verify accuracy and performance of the published algorithms by comparing the output results published by the authors and the results gathered and compiled by this study. Features like contrast, correlation, homogeneity, entropy, energy along with standard deviation, range, skewness are calculated from Gray-Level Co-occurrence Matrix (GLCM) technique. Study also implement all algorithms published by the authors and tested with common dataset containing digital images of X-ray femur (left and right leg femur; both). The research concludes that the standard deviation, image contrast and specifically energy with entropy plays a vital role in determining the disease by performing Haralick features textural analysis on plain (Non-DEXA) radiographs.

中文翻译：

---

图像处理在X射线骨病检测中的应用

摘要

这项研究比较了已发表的算法，用于检测骨骼疾病，尤其是骨质疏松症（其特征是由于微体系结构退化而导致的骨矿物质密度和孔隙率低），并基于作者选择的数据集声称其准确性。在本研究中，通过比较作者发表的输出结果与本研究收集和编译的结果，使用通用数据集来验证已发布算法的准确性和性能。对比度，相关性，均匀性，熵，能量以及标准偏差，范围，偏度等功能都是通过灰度共生矩阵（GLCM）技术计算得出的。该研究还实现了作者发表的所有算法，并用包含X射线股骨（左，右腿股骨；两者）的数字图像的通用数据集进行了测试。